Bibliographic record
Abstract
The SDGs provide a universal and ambitious framework for addressing the world’s most urgent challenges. The SDGs aim to end poverty, protect the planet and ensure peace and prosperity for all global citizens by 2030 and they require significant traction in the next seven years to meet their targets. As institutions at the forefront of knowledge access, dissemination and mobilization, academic libraries are uniquely positioned to support and advance sustainable development through their collections, services, and programming, as well as through their operational practices at the heart of campus.\nIn 2021, Sheridan became Ontario’s first institution to sign the SDG Accord, the postsecondary sector’s collective international response to the SDGs. In support of the Accord, Sheridan library has committed resources over the past two years to support Sheridan in meeting its commitment. This report examines the ways in which Sheridan library is specifically taking action on the SDGs and includes a roadmap on how the library will continue its SDG work moving forward.\nWe recognize that the challenges being addressed by the SDGs are interconnected and complex and require a holistic approach. In recognition of this, a collective effort is necessary in addressing this shared global responsibility. Sheridan library is leveraging existing partnerships and continuously building new collaborations in support of the goals. We invite readers of this report to identify potential points of collaboration with our library team. Be assured that our team is ready, capable and willing to work with you to design and implement creative and impactful solutions for SDG challenges. Together, we can achieve the goals and ensure a better future for current and future generations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.138 | 0.117 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".